Apparatus and method for parameterizing a plant

ABSTRACT

An apparatus for parameterizing a plant includes a recorder for recording a three-dimensional data set of the plant including not only volume elements of non-covered elements of the plant, but also volume elements of elements of the plants that are covered by other elements, and a parameterizer for parameterizing the three-dimensional data set for obtaining plant parameters.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of copending InternationalApplication No. PCT/EP2014/059949, filed May 15, 2014, which isincorporated herein by reference in its entirety, and additionallyclaims priority from German Application No. 10 2013 209 109.5 filed May16, 2013, which is also incorporated herein by reference in itsentirety.

BACKGROUND OF THE INVENTION

The present invention relates to the detection of plants and, inparticular, to the parameterization of plants for agriculturaltechnology.

In agricultural technology, detecting plants is of significance whereinherein the so-called phenotyping of plants is to be mentioned. Forthree-dimensional detection of objects, different methods are common,such as strip light methods or light-slit methods. These methods providea high spatial three-dimensional resolution. However, with regard toillumination, the same depend on defined environmental conditions. Inthe strip light method, different light patterns have to be subsequentlyprojected onto the object, while in the light-slit method only onecontour line is detected at a given time. Thus, for three-dimensionaldetection, the object has to be scanned.

Parameter extraction and, in particular, model-based parameterextraction are necessitated for phenotyping plants. Phenotyping is thederivation of a structural description from the appearance of a plant.Currently, phenotyping is an object of research in modern agriculturalscience, since by now the same is an important tool in agriculturalfields, such as plant cultivation or plant production.

Two steps are necessitated for phenotyping. The first step is detectionor capturing. First, a measurement system quantitatively detects thestructural characteristics of a plant. For fast automated recording ofthe plant structure, imaging methods are suitable, wherein in particularfor detecting the surface geometry of a plant mostly active or passive,typically optical 3D detection methods are used. These are, for example,laser light-slit or time-of flight sensor systems or stereoscopicsystems by means of optical cameras. The second step is featureextraction. Normally, the measured values do not correspond toillustrative features of the plant structure. Thus, in the second step,transformation of the measurement values to relevant features takesplace. Since the detected amount of data is generally quite large,normally, data reduction takes place in this step. For deriving complexleaf parameters from a measured point cloud, model-based featureextraction is suitable due to the flexible adaptation to differentpurposes of application. These parameters can be used, for example, fordescribing effects of a change in a genome of the plant on itsappearance.

EP2422297B1 describes a concept where the plant is detectedthree-dimensionally in color and subsequently a leaf model is adapted tothe measurement data. The leaf model is described by a number ofparameters. The parameters calculated while adapting the model to themeasurement data serve to describe the plant. Thereby, for example, theeffect of a change in the genome of the plant on the habit of growth ofthe plant can be described parametrically.

When using the imaging optical measurement methods for detecting the 3Dstructure, it is problematic that only the optically accessible part ofthe plant can be detected. Object areas covered by other parts cannot beoptically detected. This is undesirable, in particular when detectingplants, since leaves frequently cover one another as it is in particularthe case with dense positioning of the leaves or tight tillering of theplant structure.

Missing object areas result in wrong measurement values. The determinedleaf area of an only a partly detected plant, for example, does notcorrespond to the actual leaf area. On the other hand, an only partlydetected plant makes the usage of complex feature extraction methods byusing a model-based approach impossible when the same are based onpreconditions about completely detected leaves. For example, if changesin the genome of the plant only have an effect on those leaves that areoptically not accessible, the influence of the change in the genome onthe habit of growth cannot be detected with this procedure.

Generally, it can be said that complete detection with optical means isimpossible for plants that do not consist of very few leaves, due tounavoidable coverages.

SUMMARY

According to an embodiment, an apparatus for parameterizing a plant mayhave: a recorder for recording a three-dimensional data set of theplant, which does not only include volume elements of non-coveredelements of the plant, but also volume elements of elements of the plantthat are covered by other elements; a parameterizer for parameterizingthe three-dimensional data set for acquiring plant parameters, whereinthe parameterizer is implemented to convert the three-dimensional dataset into a point cloud, wherein the point cloud only includes points ona surface of the plant or points of a volume structure of the plant,wherein the parameterizer is further implemented to segment thethree-dimensional point cloud into single elements of the plant, whereina single element is a leaf, a stem, a branch, a trunk, a blossom, afruit or a leaf skeleton, and wherein the parameterizer is implementedto calculate, by using a single-element model, parameters for the singleelement by adapting the single-element model to the single element.

According to another embodiment, a method for parameterizing a plant mayhave the steps of: recording a three-dimensional data set of the plant,which does not only include volume elements of non-covered elements ofthe plant, but also volume elements of elements of the plant that arecovered by other elements; and parameterizing the three-dimensional dataset for acquiring plant parameters, wherein parameterizing includes:converting the three-dimensional data set into a point cloud, whereinthe point cloud only includes points on a surface of the plant or pointsof a volume structure of the plant, segmenting the three-dimensionalpoint cloud into single elements of the plant, wherein a single elementis a leaf, a stem, a branch, a trunk, a blossom, a fruit or a leafskeleton, and calculating, by using a single-element model, parametersfor the single element by adapting the single-element model to thesingle element.

Another embodiment may have a computer program for performing theinventive method for parameterizing a plant when the computer programruns on a computer or processor.

The present invention is based on the knowledge that in contrary toincomplete optical detections a three-dimensional data set of the plantis to be detected, which does not only comprise volume elements ofelements of the plant that are visible to the outside but also volumeelements of covered elements of the plant. For that purpose, forexample, computer tomographic methods, such as X-ray-CT methods ormagnetic resonance tomography can be used. Thereby, the plant iscompletely detected and the resulting measurement data describe singleelements, such as leaves, stems, branches, trunks, blossoms or fruits ofthe plant completely, independent of whether the respective singleelement has been covered by another single element of the plant or not.Thereby, in subsequent, for example model-based parameter extraction,correct parameterization can be performed for describing the habit ofgrowth of the plant, even for covered parts of the plant.

Since X-ray computer tomography, is based, for example, on X-raytransmission images, structures of a plant that are optically coveredcan be detected. The same applies for magnetic resonance tomography andfor other transmission methods or other three-dimensional completedetection methods. It has been found out that X-ray computer tomographyallows the mapping of plant structures in a high-contrast and detailedmanner in the X-ray image by means of a suitable configuration, suchthat the plant structures can be easily separated from the background,for example air, in the three-dimensional reconstruction by means of aprocessor processing the different tomographic images.

Above this, it is advantageous to perform parameter extraction by meansof model-based extraction by using a general or specific leaf model,wherein a general leaf model is not tailored for a specific leave shape,while a specific leaf model is additionally characterized, for example,by previous knowledge on the examined plant. Thus, for example, tomatoeson the one hand or turnips on the other hand have clearly different leafshapes and thus it can be advantageous, when it is known from the startthat the examined plant is, for example, a tomato, to use a leaf modelsuitable for tomatoes, while, when it is known from the start that theplant to be examined is a turnip, to use a leaf model for turnips. Abovethis, model-based feature extraction is advantageous since the same canconsider the high degree of detail of the three-dimensional completedata description in order to extract manifold and precise leaf features.On the other hand, this concept provides reliable leaf features, evenwith measurement data having only little detail. Measurement data havinglittle detail can be obtained, for example, in that a computertomography having X-rays is performed, wherein the X-ray dose emittedonto the plant is relatively reduced, for example when a lower localresolution is selected.

Thus, in an embodiment of the present invention, for obtaining computertomography data, usage of X-ray cameras having a respectively coarsepixel resolution is advantageous. Thereby, the radiation dosenecessitated for obtaining the measurement data can be heavily reduced.This can be necessitated in particular for multiple measurements, forexample for determining the temporal curve of the plant growth forpreventing damage of the plants by the x-radiation. Damaging the plantby x-radiation could have an effect on the plant growth to be monitored,which is to be prevented. A particularly low radiation dose can beobtained, for example, when using X-ray cameras having a scintillatorscreen converting the x-radiation into visible light, wherein thevisible light of the scintillator screen is mapped onto one or severalCCD cameras. By analog or digital binning, i.e., by analog or digitaladdition of adjacent pixel information to a super pixel, the radiationdose necessitated for imaging can be heavily reduced, accompanied by arespective loss of local resolution. Since CCD cameras, however, have avery high resolution, resolution reduction of the CCD cameras, forexample by the factor 10 or the factor 20 is very unproblematic, inparticular when the plant parameters are obtained subsequently by meansof model-based parameter extraction, especially since previous knowledgeon the plant or the plant leaves has already been incorporated in themodel used for parameter extraction.

In an embodiment of the present invention, the three-dimensional plantstructure is detected first by an X-ray CT system or by a magneticresonance tomography system. The volumetric data set is then convertedinto a cloud of three-dimensional points, for example, by extracting anexplicit isosurface representation of the plant surface by means of themarching cubes method or an alternative method, such as a thresholdmethod. In a further step, the three-dimensional points of the cloud aresegmented from three-dimensional points into single elements, such assingle leaves, for example by finding point clusters. In particular in acompletely detected 3D reconstruction representation that includes notonly the optically accessible but also the covered structures, this issignificantly more accurate and simpler than with incompletely detectedsurface data. In a further step, the leaf parameters are extracted bymeans of a general model or a model already adapted for the individualplant.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention will be detailed subsequentlyreferring to the appended drawings, in which:

FIG. 1 is a block diagram of the apparatus for parameterizing a plant;

FIG. 2a is a block diagram of an implementation of the recorder of FIG.1;

FIG. 2b is an illustration of a structure of the X-ray camera of FIG. 2a;

FIG. 3 is a block diagram of an implementation of the parameterizer ofFIG. 1;

FIG. 4 is an application of the extracted parameters at the example of aphenotyper;

FIG. 5 is an implementation of the segmentation means of FIG. 3;

FIG. 6 is an implementation of the single element model adaptor of FIG.3;

FIG. 7a is an illustration of the three-dimensional point cloud of theplant surface as obtained, for example, by the point cloud converter ofFIG. 3;

FIG. 7b is an enlarged polygon representation of a leaf as obtained bythe point cloud converter of FIG. 3 when the same applies the marchingcubes method; and

FIG. 8 is a two-dimensional representation of a CT volumerepresentation, i.e., a complete volume representation with coveredstructures of a plant as obtained, for example, by the recorder of FIG.1.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows an apparatus for parameterizing a plant. A recorder 100 isimplemented to record a three-dimensional data set of a plant 101. Thethree-dimensional data set does not only have volume elements of theplant that are visible to the outside or optically visible, but alsoincludes volume elements of covered elements of the plant. Thus, thethree-dimensional data set not only detects a representation ofnon-covered elements of the plant but also volume elements of elementsof the plant that are covered by other plant elements. This data set isalso referred to as complete data set.

This complete volume representation which can be given, for example, asvoxel grid, wherein each voxel comprises both a three-dimensionalcoordinate with respect to an originating coordinate as well as at leastone or several intensity values, is then fed into a parameterizer 200that is implemented to parameterize the three-dimensional data set, forexample the voxel grid, for obtaining plant parameters 201. The completevolume representation, i.e., the volume representation 102 withnon-covered and covered plant parts is converted into plant parameters201 by the parameterizer 200. Thereby, the data reduction to relevantparameters necessitated, for example, for phenotyping is obtained. Whenit is exemplarily assumed that the recorder provides a volumerepresentation with 1000 elements in length, 1000 elements in width and1000 elements in height, the volume representation has 109 voxels. Whenit is further assumed that the plant includes 10 leaves and for each ofthe 10 leaves, for example, 10 parameters are obtained, the 1 billionvoxels at the output of block 100 of FIG. 1 will merely be 100 parametervalues at the output of block 200.

FIG. 2a shows an implementation of the recorder 100 of FIG. 1. Inparticular, the recorder 100 of FIG. 1 is implemented to perform anX-ray CT method or a magnetic resonance tomography method for obtainingthe three-dimensional volume representation. An X-ray or MRT system 110provides the volume representation 102 or 111 in FIG. 2b . The volumerepresentation has individual volume elements, wherein each volumeelement comprises a coordinate in relation to one and the same originand advantageously only a single intensity. FIG. 2b shows animplementation of an X-ray camera 110 of FIG. 2a . The X-ray cameraincludes an X-ray source 113 transmitting X-rays 114 through a plant101. The X-rays reach a scintillator screen 10. Further, the cameraincludes a camera carrier 12 having an array of camera mounts 14. Thecamera mounts are implemented for being able to mount a single camera 16in a camera mount. The camera mount 12 is advantageously implemented asa plate having bores arranged in a predetermined pattern, wherein anadjustment screw, for example, is provided at each bore for inserting acylindrical camera, whose diameter is slightly smaller than the diameterof the bore, into the bore and to adjust the same by means of anadjustment screw. Alternative options can also be used, for examplecamera carriers having slightly conical bores for placing single camerashaving a slightly conical outside diameter into the conical bores, suchthat no adjustment screws or other mounts are necessitated, since merelythe press fit force of the camera into the camera carrier is sufficient.

The camera includes an array of optical single cameras 16, wherein eachoptical single camera is mounted on an allocated camera mount 14.Further, each single camera includes a light sensor and an opticsmapping means, wherein the light sensor and the optics mapping means areeffective to capture a partial area of the screen area of the screen 10with a specific resolution. Thus, each single camera 16 provides asingle image with a predetermined resolution.

It should be noted that optics assemblies could be implemented in anyway. For financial reasons, a lens assembly, which can include one orseveral lenses, depending on the implementation, is advantageous asoptics assembly. Alternative optics assemblies include mirrorassemblies, fiber optics, etc., or also a combination of differentoptical mapping means.

Further, the camera includes an image processing means 18 for processingthe digital single images of the array of optical single cameras 16 forgenerating the optical image of the screen with the predeterminedoverall resolution. In detail, the image processing means 18 iseffective to submit the digital single images to a correction forreducing orientation inaccuracy and/or parameter variations in the arrayof optical single cameras 16 and advantageously to completely eliminatethe same. For correcting a single image, during calibration precedingcapturing, a specific correction rule 20 is used, which is typicallystored on a suitable memory medium in the image processing means 18 oreven hard-wired. Thus, the correction takes place with the ruledetermined during calibration with a correction resolution that ishigher than the predetermined overall resolution of the optical overallpicture desired in the end, and which is lower or equal to the singleresolution by which the optical single cameras provide single images,although this is not essential. Finally, the image processing means iseffective to obtain corrected single images or a corrected overallimage. Combining the single images to the overall image can thus takeplace after correcting the single images with the correction ruledetermined for each single image, i.e. for each single camera. Thus, atthe output of the image processing means 18, after the CT or MRTreconstruction, a volume picture including all information, i.e. alsothe covered structures, is obtained. In this implementation, the fact isused that by using several cameras, the available image elements(pixels) increase proportionally to the number of cameras. Frequently,however, no greater number of pixels than the one that a single camerawould provide is necessitated. If, for example, four optical cameras areused, in this case, four pixels each can be added up. This isparticularly advantageous when the charge can be added up already on thesensor, as it takes place in CCD sensors by the so-called analogbinning. In this case, the charge has to be read out electronically onlyonce and thus the electronic noise generated by this process occurs onlyonce. Thus, the overall signal-to-noise ratio is improved compared tothe case when each pixel is read out individually and digitally added.

Due to the fact that the signal-to-noise ratio is significantlyincreased by analog binning, the dose of the X-ray source can bereduced, for obtaining a necessitated signal-to-noise ratio at theoutput of the single cameras performing analog binning for obtaining,from individual pixels, a super pixel having a poor local resolution.

Thus, the single CCD cameras have a high resolution. After analogbinning, a lower resolution is obtained, but with a heavily improvedsignal-to-noise ratio, such that the radiation dose of the X-ray source113 can be accordingly reduced, for sparing the plants or for makingsure that damage of the plant by the X-radiation is prevented withregard to the plant growth to be monitored when several pictures of theplant are necessitated.

In the embodiment shown in FIG. 2b , illustrating the X-ray camera 110,the screen 10 is a scintillator screen where the X-ray light isconverted to visible light. The array of single cameras 16 lies behindthe scintillator screen 10, wherein each single camera captures or mapsone part of the scintillator screen. For specific tasks, where usually aline scan camera is used, the array of optical single cameras is reducedto a one-dimensional array including a linear arrangement of opticalsingle cameras. The areas mapped by the single optical cameras caneither be immediately adjacent or slightly overlap each other forreducing the adjustment effort which will typically arise on themechanical side. If the partial images or single images overlap, it isadvantageous to perform electronic correction as has already beenillustrated. With regard to further implementations of the camera,reference is made to EP 1 586 193 B1, which is incorporated herein byreference. FIG. 3 shows an implementation of the parameterizer 200according to an embodiment of the present invention.

The parameterizer includes a point cloud converter 220, a segmentationmeans 240 and a single element model adapter 260. The point cloudconverter 220 is implemented to receive the three-dimensional volumerepresentation 102 with covered and non-covered plant elements. From theindividual voxel elements, the point cloud converter 220 generates apoint cloud 221 comprising points on a surface of the plant. Each pointis now only represented by its coordinate, which is derived from thecoordinate of a respective volume element or corresponds to the same. Inone of the implementations, the derivation of the coordinate of a pointof the point cloud 221 means that the center or focus of a volumeelement is taken as a coordinate of a point. However, the point cloud221 does not have to correspond one-to-one to single volume elements.Instead, for example in the marching cubes method, interpolation isperformed, such that when it is determined that two adjacent pixels aresuch that one pixel is an air pixel and the other pixel a plant pixel,under predetermined prerequisites as known in the art, interpolation isperformed between these two voxels, wherein the point cloud is thenillustrated by interpolated volume elements. Deriving the coordinates ofa volume element can thus also be an interpolation of adjacent volumeelements. The marching cubes method is a known algorithm for calculatingisosurfaces in 3D computer graphics. The same approximates voxelgraphics by polygon graphics. The first description can be found inWilliam E. Lorensen, Harvey E. Cline: “Marching Cubes: A High Resolution3D Surface Construction Algorithm”, Computer Graphics, Vol. 21, No. 4,July 1987, pp. 163-169.

However, alternative procedures in the point cloud converter can beused, such as a simple threshold decision based on the intensity. Ahigher intensity of a volume element shows that the same was air, whilea lower intensity of a volume element shows that absorption of theX-radiation has already taken place in the plant and the same is thus avolume element belonging to the plant or to the plant surface. Athreshold decision would be to eliminate all volume elements having agreater intensity than a predetermined value, such that only thosevolume elements belonging to a plant element remain.

In particular in leaves or flat plant elements, the voxels alreadynearly represent a surface, since a leaf will typically have a thicknessof one or several voxels. Other leaf structures, such as the leafskeleton, clearly visible in FIG. 8, also called leaf veins, have avolumetric structure and no area structure. The leaf skeleton canalready present valuable parameters useful for phenotyping. Then,segmentation and parameter extraction are performed from the volumetricdata directly or after point cloud conversion, for describing the plantskeleton parametrically.

The result of the point cloud converter 220 is a cloud ofthree-dimensional points, which can either be given as single points or,as it is the case in the marching cube method, can be an explicitisosurface representation or a polygon area representation of the plantsurface. The marching cube method is particularly advantageous since thesame performs, in addition to differentiating between volume elements ofthe plant on the one hand and of the background on the other hand, alsointerpolation and thus states exactly the border or interface betweenplant and background, i.e. air. Thus, in the implementation of the pointcloud converter, thus, a complete surface of the plants in front of thebackground is obtained from the complete volume data set, wherein thissurface includes all leaf stems, branches, etc.

This area representation is then fed into the segmentation means 240that is implemented to convert the three-dimensional point cloud asobtained by block 220 into single elements as illustrated at 241.Depending on the implementation, the single elements can be severalsmall point clouds, for example for a leaf, a stem, a trunk, a blossomor a fruit of the plant or can already be polygon areas in whateverrepresentation.

Then, the segmented representation 241 is fed into the single elementmodel adapter 260 that is implemented to extract, by using a generalsingle element model or a single-element model already specified byprevious knowledge about the plant, the parameters for each singleelement by adapting the single element model to the respective segmentedsingle element.

On the output side, the single-element model adapter provides theparameters 260 for each single element, e.g. the parameters for eachleaf. Depending on the implementation of the present invention and theapplication, the parameter, in particular for a leaf, can be a leaflength, a leaf width, a leaf area, an inclination of a leaf with regardto the stem, an orientation of the leaf with regard to a plant mainaxis, i.e. in the top view an orientation to the top, bottom, left orright, etc., a twist with regard to a leaf axis, a leaf arch, a leafshape or a leaf outline. Further parameters are in particularindications for errors in the leaf outline, for example by pestinfestation, etc. Further useful plant parameters include corrugationsat the edge or in the area of the leaf, a folding of the leaf halves, orrolling-in of the leaf along or transversal to the leaf axis.

The parameters 261 obtained in FIG. 3 for many leaves can then be fedinto a phenotyper 400. The phenotyper 400 provides a parametricdescription of the whole plant, stating as values, for example, thenumber of leaves, the average value and/or the standard deviation of theindividual leaf parameters in allocation to a specific change in thegenome. Thus, an agricultural scientist will now able to obtain, basedon the parametric description of the whole plant at the output of block400 of FIG. 4, a simple allocation and knowledge of which change in thegenome results in which effects on leaf orientation, leaf area, etc. Inparticular, the leaf orientation is of great interest in that anorientation closer to the sun or in the optimum right angle to the suncauses high photosynthesis activity of the leaf and hence results in agreater and faster growth of the leaf.

Although FIG. 3 represents an implementation where the segmentationtakes place after point cloud conversion, in another implementation,alternatively or additionally, segmentation can be performed directlywith the three-dimensional representation, to subsequently convert thesegmented elements into a point cloud consisting only of one singleelement, or to directly parameterize the single element still consistingof voxels. For segmenting the three-dimensional data set, the fact, forexample, that stems of leaves are normally thinner than the leaf can beused, such that segmentation can be performed by “cutting off” or“separating” the data set at a thinner location.

FIG. 5 shows a flow diagram for an implementation of the segmentationmeans 240 of FIG. 3. In a step 242, a plurality of polygon areas isdetermined to which a point of the point cloud 221 belongs. In a step243, a normal is calculated on each of these polygon areas. The polygonareas are triangular areas or areas having more corners, such as four,five, six, . . . corners. A normal on the polygon area is used forquantifying the orientation of this polygon area. Thereupon, in a step244, the size of each of these polygon areas is calculated.

The size of each polygon area results in a respective weighting orrespective weighting factor 245 allocated to this polygon area, whilethe individual normals are shown on the respective polygon area at 246.Both the weighting factors 245 and the normals 246 are used in the step247 for performing weighted averaging of the individual area normals246. The greater the area of a polygon area, the higher the weightingfactor for this polygon area, such that in the step of weightedaveraging 247, the polygon areas or the normals of the polygon areas forwhich a greater size has been calculated have a greater influence.Thereby, a normal 248 is obtained for each point of the point cloud 221of FIG. 3. Based on the consideration of the normal for the respectivepoints, and in particular also for adjacent points as illustrated at249, the actual segmentation is performed in step 250, in that theindividual normals are examined for adjacent points.

In step 244, where the plurality of polygon areas is determined, towhich a point belongs, a sphere having a specific diameter can be formedaround a point for obtaining all polygon areas to which a point belongs.Alternatively, a circle can be formed, when a two-dimensional area ofthe plant surface exists, as it will frequently be the case forsurfaces. Above that, it should be noted that, depending on theimplementation, the step of averaging 249 can also be performed in anunweighted manner, such that the size of the polygon areas is notnecessarily incorporated in the calculation.

In the step 250 of segmenting, the individual normals of adjacent pointscan be examined, wherein depending on the implementation, a thresholddecision or a more expensive method can be used. One option is thecalculation of differences of the normal orientation between adjacentpoints for determining, when the difference lies within a specificdeviation, that the points belong to the same surface, while when thedifferences between adjacent points are greater than a deviationaccording to a predetermined threshold, the same belong to differentsingle elements. When considering, for example, a leaf arranged at astem, a very significant normal change will occur exactly at theposition where the leaf leaves the stem, since this is a specific edge.On the other hand, leaves typically also have leaf arches, such thatspecific deviations from point to point of the sheet normal when thesame take place slowly or relatively slow indicate that all these pointsbelong to a specific leaf. Depending on leaf and effort, furthercriteria can be incorporated, such as previous knowledge on an expectedleaf shape, etc., in order to check and to verify the plausibility ofthe segmentation decision in block 250 and to possibly correct adecision.

In the marching cubes method, for example, a polygon representationalready exists. If this is not the case, the normal can still be used.For this, the local neighborhood around a point is found by a spherearound this point. All points lying within the sphere having a definedradius form this local neighborhood. Then, a plane fitted into thisneighborhood and the normal on this plane is the normal allocated tothat point. Then, segmentation is performed based on the normal asdescribed.

FIG. 6 shows an implementation of the single-element model adapter 260of FIG. 3. A parameterized single element or leaf model having variableparameters is provided for a specific element in a block 262. Then, themodel of block 262 is fed into a leaf model fitter 264 together with thepoint cloud 263 that has been obtained by step 250 of FIG. 5. The leafmodel fitter is implemented to vary the parameters of the model providedin block 262 for so long according to specific optimization criteriauntil a smallest deviation between model and reality, i.e. the pointcloud 263 of the single element is obtained. In particular, a variationof the variable parameters can take place in that, for example,according to the method of least error squares, a smallest deviationbetween the individual points of the parameterized model and the pointcloud 263 of the single element is obtained. The results are theparameter sizes 265 that have been obtained from leaf model fitting 264.These result parameters can then be output and represent the plantparameters 201 of FIG. 1. Other leaf model fitting methods can be used,such as models operating in the sense of an “elastic band” fixed atrespective supporting points, such that deviations at the leaf edge orin the leaf area are obtained with respect to an expected geometry,which could not be detected in a simpler model. When a leaf has, forexample, an indentation because of pest infestation, which might havebeen caused by pests removing the piece of the leaf at the edge, such amodel would parameterize this edge or this deviation from the optimumgeometry as well. In contrast to a simpler model with expected leafgeometry, such a model additionally comprises free parameters, oradditional parameters can be used in the optimization process, such thatthe number of output parameters in such a model depends on the pointcloud of the single element 263 and is not given in advance. Furtherfitting methods operating alternatively to the two above-describedmethods can also be used for obtaining the result parameters 201 of FIG.1.

FIG. 7a shows an illustration of the 3D point cloud of a plant surfaceas obtained at the output of block 220 of FIG. 3. Here, four leaves 701,702, 703, 704 that have already been separated from the background canbe clearly seen. The three-dimensional point cloud illustrated in FIG.7a , however, is not yet segmented into the single leaves. Further, thegrowth substrate, i.e. the ground 705, is also not segmented. Based onnecessitated geometric parameters, i.e. based on height values lyingbelow a specific threshold, the earth or ground 705 can be easilyeliminated when necessitated.

In contrast, FIG. 8 shows a two-dimensional illustration of a completeCT volume representation with covered and non-covered structures for theplant shown in FIG. 7a , wherein again the four leaves can be seenclearly. In complicated plants having a dense leaf position such thatleaf elements are covered in any viewing direction, the completereconstruction in the sense of FIG. 8 would also include all thesecovered structures.

FIG. 7b shows an enlarged polygon representation of a leaf as obtainedfrom the marching cubes method, wherein FIG. 7b schematically showsindividual polygon areas of different sizes, wherein this representationin FIG. 7b or the underlying data set can be used for obtainingsegmentation in that the leaf is separated from its stem or separatedfrom the background 705, in order to then perform with the single leaf,i.e. the single element, a parameter fitting method as described basedon block 264 in FIG. 6.

While some aspects have been described in the context of an apparatus,it is obvious that these aspects also represent a description of therespective method, such that a block or device of an apparatus can alsobe seen as a respective method step or as a feature of a method step.Analogously, aspects described in the context of a method step or as amethod step also represent a description of a corresponding block ordetail or feature of a corresponding apparatus. Some or all of themethod steps may be executed by (or using) a hardware apparatus, likefor example, a microprocessor, a programmable computer or an electroniccircuit. In some embodiments, some or several of the most importantmethod steps may be executed by such an apparatus.

Depending on certain implementation requirements, embodiments of theinvention can be implemented in hardware or in software. Theimplementation can be performed using a digital storage medium, forexample a floppy disk, a DVD, a Blu-Ray, a CD, a ROM, a PROM, an EPROM,an EEPROM or a FLASH memory, a hard drive or other magnetic or opticalmemory having electronically readable control signals stored thereon,which can cooperate or cooperate with a programmable computer systemsuch that the respective method is performed. Therefore, the digitalstorage medium may be computer readable.

Some embodiments according to the invention comprise a data carrierhaving electronically readable control signals, which are capable ofcooperating with a programmable computer system, such that one of themethods described herein is performed.

Generally, embodiments of the present invention can be implemented as acomputer program product with a program code, the program code beingoperative for performing one of the methods when the computer programproduct runs on a computer.

The program code may for example be stored on a machine readablecarrier.

Other embodiments comprise the computer program for performing one ofthe methods described herein, wherein the computer program is stored ona machine readable carrier.

In other words, an embodiment of the inventive method is, therefore, acomputer program having a program code for performing one of the methodsdescribed herein, when the computer program runs on a computer.

A further embodiment of the inventive methods is, therefore, a datacarrier (or a digital storage medium, or a computer-readable medium)comprising, recorded thereon, the computer program for performing one ofthe methods described herein.

A further embodiment of the inventive method is, therefore, a datastream or a sequence of signals representing the computer program forperforming one of the methods described herein. The data stream or thesequence of signals may for example be configured to be transferred viaa data communication connection, for example via the Internet.

A further embodiment comprises a processing means, for example acomputer, or a programmable logic device, configured to or adapted toperform one of the methods described herein.

A further embodiment comprises a computer having installed thereon thecomputer program for performing one of the methods described herein.

A further embodiment according to the invention comprises an apparatusor a system configured to transfer a computer program for performing oneof the methods described herein to a receiver. The transmission can beelectronical or optical. The receiver may, for example, be a computer, amobile device, a memory device or the like. The apparatus or system may,for example, comprise a file server for transferring the computerprogram to the receiver.

In some embodiments, a programmable logic device (for example a fieldprogrammable gate array, FPGA) may be used to perform some or all of thefunctionalities of the methods described herein. In some embodiments, afield programmable gate array may cooperate with a microprocessor inorder to perform one of the methods described herein. Generally, in someembodiments, the methods are performed by any hardware apparatus. Thesame can be a universally usable hardware, such as a computer processor(CPU) or hardware specific for the method, such as an ASIC.

While this invention has been described in terms of several advantageousembodiments, there are alterations, permutations, and equivalents whichfall within the scope of this invention. It should also be noted thatthere are many alternative ways of implementing the methods andcompositions of the present invention. It is therefore intended that thefollowing appended claims be interpreted as including all suchalterations, permutations, and equivalents as fall within the truespirit and scope of the present invention.

The invention claimed is:
 1. An apparatus for parameterizing a plant,comprising: a recorder for recording a three-dimensional data set of theplant, which does not only comprise volume elements of non-coveredelements of the plant, but also volume elements of elements of the plantthat are covered by other elements; a parameterizer for parameterizingthe three-dimensional data set for acquiring plant parameters, whereinthe parameterizer is implemented to convert the three-dimensional dataset into a point cloud, wherein the point cloud only comprises points ona surface of the plant or points of a volume structure of the plant,wherein the parameterizer is further implemented to segment thethree-dimensional point cloud into single elements of the plant, whereina single element is selected from the group consisting of a leaf, astem, a branch, a trunk, a blossom, a fruit skeleton, and a leafskeleton, and wherein the parameterizer is implemented to calculate, byusing a single-element model, parameters for the single element byadapting the single-element model to the single element, wherein theparameterizer is implemented to determine surface normal for areaelements of a surface of the plant for segmenting, and to determine forthe area elements, based on an examination of the surface normal,whether a point belongs to a single element or not, and wherein theparameterizer is implemented to determine, for segmenting, for each ofthe points a local neighborhood around a point, to fit a plane into thepoints of the local neighborhood and to determine a normal onto thisplane which is the normal for this considered point.
 2. The apparatusaccording to claim 1, wherein the recorder is implemented to perform anX-ray computer tomography method or a magnetic resonance tomographymethod for acquiring the three-dimensional data set, wherein a volumeelement of the three-dimensional data set comprises a three-dimensionalcoordinate and at least one intensity value.
 3. The apparatus accordingto claim 1, wherein the parameterizer is implemented to represent thepoints of the point cloud only by a coordinate derived from thecoordinate of a respective volume element or corresponding to the same.4. The apparatus according to claim 1, wherein the single element is aleaf and wherein one or several plant parameters are calculated, whereinthe one or several plant parameters are selected from the groupcomprising the following plant parameters: a leaf length, a leaf width,a leaf area, an inclination of a leaf with regard to the stem, anorientation of the leaf with regard to a plant main axis, a twist withregard to a leaf axis, a leaf arch, a leaf shape, a leaf outline, errorsin an expected leaf outline, corrugations at the edge of the leaf,corrugations in the area of the leaf, a folding of the leaf halves,rolling-in of the leaf along the leaf axis, and rolling-in of the leaftransversal to the leaf axis.
 5. The apparatus according to claim 1,wherein the parameterizer is implemented to determine, by means of anintensity threshold, volume elements belonging to the plant, wherein thepoint cloud essentially comprises no points belonging to a background ofthe plant.
 6. The apparatus according to claim 1, wherein theparameterizer is implemented to determine the point cloud, whichcomprises an interface between the plant and a background, by using amarching cubes method.
 7. The apparatus according to claim 1, wherein apoint belongs to at least a plurality of polygon areas, wherein theparameterizer is implemented to determine, for each polygon area of theplurality of polygon areas to which the point belongs, a normal, and toaverage the normals of the polygon areas to which the point belongs fordetermining a normal for the point.
 8. The apparatus according to claim7, wherein the polygon areas of the plurality of polygon areas havedifferent sizes, wherein the parameterizer is implemented to performweighted averaging of the normal, such that a normal of a polygon areawith a first size is incorporated more into the weighted averaging thana normal of a polygon area having a second size, wherein the second sizeis smaller than the first size.
 9. The apparatus according to claim 1,wherein the recorder is implemented to use a scintillator screen and aplurality of single cameras and to perform analog binning with theplurality of single cameras to perform capturing with a radiationexposure that is reduced compared to a radiation exposure when usingonly one single camera.
 10. A method for parameterizing a plant,comprising: recording a three-dimensional data set of the plant, whichdoes not only comprise volume elements of non-covered elements of theplant, but also volume elements of elements of the plant that arecovered by other elements; and parameterizing the three-dimensional dataset for acquiring plant parameters, wherein parameterizing comprises:converting the three-dimensional data set into a point cloud, whereinthe point cloud only comprises points on a surface of the plant orpoints of a volume structure of the plant, segmenting thethree-dimensional point cloud into single elements of the plant, whereina single element is selected from the group consisting of a leaf, astem, a branch, a trunk, a blossom, a fruit skeleton, and a leafskeleton, and calculating, by using a single-element model, parametersfor the single element by adapting the single-element model to thesingle element, wherein the parameterizing comprises determining surfacenormals for area elements of a surface of the plant for segmenting, anddetermining for the area elements, based on an examination of thesurface normal, whether a point belongs to a single element or not, andwherein the parameterizing comprises determining, for the segmenting,for each of the points a local neighborhood around a point, fitting aplane into the points of the local neighborhood and determining a normalonto this plane which is the normal for this considered point. 11.Non-transitory storage medium having stored thereon a computer programfor performing the method for parameterizing a plant according to claim10 when the computer program runs on a computer or a processor. 12.Apparatus for parameterizing a plant, comprising: a recorder forrecording a three-dimensional data set of the plant, which does not onlycomprise volume elements of non-covered elements of the plant, but alsovolume elements of elements of the plant that are covered by otherelements; a parameterizer for parameterizing the three-dimensional dataset for acquiring plant parameters, wherein the parameterizer isimplemented to convert the three-dimensional data set into a pointcloud, wherein the point cloud only comprises points on a surface of theplant or points of a volume structure of the plant, wherein theparameterizer is further implemented to segment the three-dimensionalpoint cloud into single elements of the plant, wherein a single elementis a leaf, a stem, a branch, a trunk, a blossom, a fruit or a leafskeleton, and wherein the parameterizer is implemented to calculate, byusing a single-element model, parameters for the single element byadapting the single-element model to the single element, wherein therecorder is implemented to use a scintillator screen and a plurality ofsingle cameras and to perform analog binning with the plurality ofsingle cameras to perform capturing with a radiation exposure that isreduced compared to a radiation exposure when using only one singlecamera.
 13. Method for parameterizing a plant, comprising: recording athree-dimensional data set of the plant, which does not only comprisevolume elements of non-covered elements of the plant, but also volumeelements of elements of the plant that are covered by other elements;and parameterizing the three-dimensional data set for acquiring plantparameters, wherein parameterizing comprises: converting thethree-dimensional data set into a point cloud, wherein the point cloudonly comprises points on a surface of the plant or points of a volumestructure of the plant, segmenting the three-dimensional point cloudinto single elements of the plant, wherein a single element is a leaf, astem, a branch, a trunk, a blossom, a fruit or a leaf skeleton, andcalculating, by using a single-element model, parameters for the singleelement by adapting the single-element model to the single element,wherein the recording comprises using a scintillator screen and aplurality of single cameras and performing analog binning with theplurality of single cameras to perform capturing with a radiationexposure that is reduced compared to a radiation exposure when usingonly one single camera.
 14. Non-transitory storage medium having storedthereon a computer program for performing the method for parameterizinga plant according to claim 13, when the computer program runs on acomputer or a processor.